suppressMessages(library(Seurat))
suppressMessages(library(ggplot2))
suppressMessages(library(scater))
suppressMessages(library(SingleCellExperiment))
suppressMessages(library(scran))
srt <- readRDS("/mnt/nmorais-nfs/marta/pB_joana/pC_data/srt-all-samples-after-qc.rds")
srt
## An object of class Seurat
## 16864 features across 40956 samples within 1 assay
## Active assay: RNA (16864 features, 3000 variable features)
## 3 dimensional reductions calculated: pca, umap, tsne
table(srt$sample)
##
## old0 old1 old3 old5 yg0 yg1 yg3 yg5
## 6199 5596 4650 3995 5524 4434 5550 5008
colorClusters <- c("#6fccc1", "#cf1fa7", "#37ee7c", "#da0322", "#ffd94a", "#207ede", "#fea111", "#b09de9",
"#b86fbb", "#150de2", "#ab3f2f", "#d49b6c", "#773aab", "#759474", "#e4b2b8", "#f88a40",
"#74b5cf", "#e6208e", "#0a3535", "#5b37c3", "#cc4025", "#d2dc7c", "#344d76", "#ba8b07",
"#2e10b6", "#4d0cb0", "#e1668a", "#47a58b", "#d7734a", "#ff58c7", "#edceef", "#a21d59",
"#8c8da5", "#ff3e5e", "#688d4b", "#214fca", "#48a8f5", "#752758", "#3b4a4a", "#11674b",
"#9a1bd9", "#16146d", "#7d7277", "#2d8cce", "#2460b8", "#0f9a8f", "#b11e90", "#54ed4f",
"#987217", "#980119", "#cf0606", "#dd182c", "#88a1e4", "#3b89ba", "#12defd", "#feba3e",
"#e31fa5", "#7b3537", "#098a95", "#1f1716", "#df6c94", "#9965ee", "#b438f9", "#0140f5",
"#e6c4a8", "#d94f7c", "#0dae56", "#b86d1d", "#0577ad", "#464551", "#6b0959", "#ccf705",
"#12271d")
Annotate cells
cell_type <- srt$RNA_snn_res.0.4
cell_type <- gsub("^20$", "Unknown 2" , cell_type)
cell_type <- gsub("^19$","Plasma cells" , cell_type)
cell_type <- gsub("^18$", "Unknown 1" , cell_type)
cell_type <- gsub("^17$", "NK Cells" , cell_type)
cell_type <- gsub("^16$", "mregDCs" , cell_type)
cell_type <- gsub("^15$", "CD11a Myeloid" , cell_type)
cell_type <- gsub("^14$", "Fibrocytes" , cell_type)
cell_type <- gsub("^13$", "Granulocytes" , cell_type)
cell_type <- gsub("^12$", "Proliferating" , cell_type)
cell_type <- gsub("^11$", "B Cells" , cell_type)
cell_type <- gsub("^10$", "ILC2" , cell_type)
cell_type <- gsub("^9$","T Cells" , cell_type)
cell_type <- gsub("^8$","Eosinophils" , cell_type)
cell_type <- gsub("^7$", "cDC1" , cell_type)
cell_type <- gsub("^6$", "DCs injury" , cell_type)
cell_type <- gsub("^5","DCs residentes" , cell_type)
cell_type <- gsub("^4$","Infiltrating Type 2" , cell_type)
cell_type <- gsub("^3$", "Macrophages resident", cell_type)
cell_type <- gsub("^2$","Infiltrating Type 1" , cell_type)
cell_type <- gsub("^1$", "Macrophages Repair" , cell_type)
cell_type <- gsub("^0$", "Macrophages intermediate" , cell_type)
table(cell_type)
## cell_type
## B Cells CD11a Myeloid cDC1
## 916 339 1299
## DCs injury DCs residentes Eosinophils
## 1705 2130 1053
## Fibrocytes Granulocytes ILC2
## 341 360 935
## Infiltrating Type 1 Infiltrating Type 2 Macrophages intermediate
## 5579 3924 9304
## Macrophages Repair Macrophages resident mregDCs
## 5873 4516 303
## NK Cells Plasma cells Proliferating
## 245 104 854
## T Cells Unknown 1 Unknown 2
## 963 148 65
srt$cell_type <- cell_type
colors <- c("deeppink", "darkorange4", "blueviolet", "darkorchid1",
"darkmagenta", "tomato", "lightgoldenrod3", "sienna3",
"green3", "red", "yellow", "green4", "blue", "navy", "mediumpurple4",
"grey", "orange3", "black", "orange", "rosybrown1", "rosybrown2"
)
DimPlot(srt, reduction = "tsne", group.by = "cell_type", cols = colors, pt.size = 1)
Subset macrophages
unique(cell_type)
## [1] "Macrophages intermediate" "Proliferating"
## [3] "Infiltrating Type 1" "Macrophages Repair"
## [5] "DCs injury" "cDC1"
## [7] "Infiltrating Type 2" "mregDCs"
## [9] "Unknown 1" "T Cells"
## [11] "CD11a Myeloid" "DCs residentes"
## [13] "Macrophages resident" "Eosinophils"
## [15] "B Cells" "Fibrocytes"
## [17] "ILC2" "Granulocytes"
## [19] "Unknown 2" "NK Cells"
## [21] "Plasma cells"
keep_macs <- c("Macrophages intermediate", "Macrophages Repair", "Infiltrating Type 2",
"Infiltrating Type 1", "CD11a Myeloid" )
srt_mac <- srt[, srt$cell_type %in% keep_macs & srt$condition == "Young"]
srt_mac
## An object of class Seurat
## 16864 features across 12313 samples within 1 assay
## Active assay: RNA (16864 features, 3000 variable features)
## 3 dimensional reductions calculated: pca, umap, tsne
table(srt_mac$cell_type, srt_mac$condition)
##
## Young
## CD11a Myeloid 111
## Infiltrating Type 1 2034
## Infiltrating Type 2 1961
## Macrophages intermediate 4984
## Macrophages Repair 3223
mac_colors <- c("darkorange4","red", "yellow", "green4", "blue", "navy" )
DimPlot(srt_mac, reduction = "tsne", group.by = "cell_type", cols = colors <- mac_colors, pt.size = 1)
sce <- readRDS("/mnt/nmorais-nfs/marta/pB_joana/pC_data/sce_all-samples-after-qc.rds")
sce$cell_type <- srt$cell_type
table(sce$condition, srt$condition)
##
## Old Young
## Old 20440 0
## Young 0 20516
sce_macs <- sce[, srt$cell_type %in% keep_macs & srt$condition == "Young"]
sce_macs
## class: SingleCellExperiment
## dim: 16864 12313
## metadata(0):
## assays(3): counts logcounts limma
## rownames(16864): Xkr4 Mrpl15 ... CAAA01147332.1 AC149090.1
## rowData names(0):
## colnames(12313): AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 ...
## TTTGATCGTTGCTCAA-1 TTTGATCGTTTCTTAC-1
## colData names(18): total_counts log10_total_counts ... doublet
## cell_type
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
table(sce_macs$cell_type, sce_macs$condition)
##
## Young
## CD11a Myeloid 111
## Infiltrating Type 1 2034
## Infiltrating Type 2 1961
## Macrophages intermediate 4984
## Macrophages Repair 3223
Perform library size normalization
mat <- assays(sce_macs)$counts
dim(mat)
## [1] 16864 12313
mat[1:5,1:5]
## 5 x 5 sparse Matrix of class "dgCMatrix"
## AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 AAACCCATCATGCTAG-1
## Xkr4 . . .
## Mrpl15 . 1 1
## Lypla1 . 2 .
## Tcea1 1 2 1
## Rgs20 . . .
## AAACGAACAGCTGGTC-1 AAACGCTAGTCATACC-1
## Xkr4 . .
## Mrpl15 . 1
## Lypla1 1 1
## Tcea1 4 4
## Rgs20 . .
gene_sum <- rowSums(mat)
gene_sum[1:5]
## Xkr4 Mrpl15 Lypla1 Tcea1 Rgs20
## 112 5443 5529 16565 149
mat <- mat[gene_sum >= 0,]
dim(mat)
## [1] 16864 12313
sce_macs <- sce_macs[gene_sum >= 0,]
total_counts <- colSums(mat)
total_counts[1:5]
## AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 AAACCCATCATGCTAG-1 AAACGAACAGCTGGTC-1
## 8691 12852 6544 20761
## AAACGCTAGTCATACC-1
## 15778
total_features <- c()
for (cell in colnames(sce_macs)){total_features <- c(total_features, sum(mat[,cell] > 0))}
total_features[1:5]
## [1] 2415 3393 2233 4163 3773
saveRDS(total_features, "/mnt/nmorais-nfs/marta/pB_joana/pC_data/total_features-mono-macs-young-without-residents.rds")
logmat <- log2(mat+1)
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 1.5 GiB
df1 <- data.frame(total_counts = total_counts,
total_features = total_features,
sample = sce_macs$sample,
sorting = sce_macs$sorting_day,
lane = sce_macs$lane)
df2 <- data.frame(sample = sce_macs$sample,
log2_total_features = log2(total_features),
log2_total_counts = log2(total_counts))
varMatrix <- getVarianceExplained(mat, variables = df1)
logVarMatrix <- getVarianceExplained(logmat, variables = df1)
plotExplanatoryVariables(
varMatrix,
variables = variables) +
ggtitle("Young - Mono/macs") +
theme(text = element_text(size=20)) +
scale_color_manual(values=c( "dodgerblue" ,"purple" , "green3","orange", "blue", "red"))
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 245 rows containing non-finite values (stat_density).
plotExplanatoryVariables(
logVarMatrix,
exprs_values="logcounts",
variables = variables) +
ggtitle("Young - Mono/macs") +
theme(text = element_text(size=20)) +
scale_color_manual(values=c("dodgerblue" ,"purple" , "green3","orange", "blue"))
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 245 rows containing non-finite values (stat_density).
make_barplot <- function(df, x, y, title = "Young - Mono/macs", ylab){
ggplot(df, aes(x=x, y=y, fill=sample)) +
geom_boxplot(show.legend = FALSE) +
theme_bw() +
theme(text = element_text(size=20), axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
ylab(ylab) + xlab("Sample") +
ggtitle(title)
}
make_barplot(df1, df1$sample, df1$total_counts, ylab = "Library size")
make_barplot(df2, df2$sample, df2$log2_total_counts, ylab = "Library size (log2)")
make_barplot(df1, df1$sample, df1$total_features, ylab = "Total features")
make_barplot(df2, df2$sample, df2$log2_total_features, ylab = "Total features (log2)")
qclust <- quickCluster(sce_macs)
sce <- computeSumFactors(sce_macs, clusters=qclust)
summary(sizeFactors(sce_macs))
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.1726 0.8203 1.0728 1.1418 1.3824 4.1643
sce <- logNormCounts(sce_macs,
size_factors = sizeFactors(sce_macs),
log = TRUE,
pseudo_count = 1,
exprs_values = "counts"
)
sce_macs
## class: SingleCellExperiment
## dim: 16864 12313
## metadata(0):
## assays(3): counts logcounts limma
## rownames(16864): Xkr4 Mrpl15 ... CAAA01147332.1 AC149090.1
## rowData names(0):
## colnames(12313): AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 ...
## TTTGATCGTTGCTCAA-1 TTTGATCGTTTCTTAC-1
## colData names(18): total_counts log10_total_counts ... doublet
## cell_type
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
mat <- assays(sce_macs)$logcounts
total_counts <- colSums((2**mat)-1)
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 1.5 GiB
total_counts[1:5]
## AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 AAACCCATCATGCTAG-1 AAACGAACAGCTGGTC-1
## 13227.27 10872.45 11450.98 10965.41
## AAACGCTAGTCATACC-1
## 11299.45
mat[1:5,1:5]
## 5 x 5 sparse Matrix of class "dgCMatrix"
## AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 AAACCCATCATGCTAG-1
## Xkr4 . . .
## Mrpl15 . 0.8843815 1.459349
## Lypla1 . 1.4286495 .
## Tcea1 1.33454 1.4286495 1.459349
## Rgs20 . . .
## AAACGAACAGCTGGTC-1 AAACGCTAGTCATACC-1
## Xkr4 . .
## Mrpl15 . 0.7791778
## Lypla1 0.6118084 0.7791778
## Tcea1 1.6381639 1.9503228
## Rgs20 . .
((2**mat)-1)[1:5,1:5]
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 1.5 GiB
## 5 x 5 Matrix of class "dgeMatrix"
## AAACCCAAGGGTTAAT-1 AAACCCACAAGTACCT-1 AAACCCATCATGCTAG-1
## Xkr4 0.00000 0.0000000 0.000000
## Mrpl15 0.00000 0.8459731 1.749843
## Lypla1 0.00000 1.6919461 0.000000
## Tcea1 1.52195 1.6919461 1.749843
## Rgs20 0.00000 0.0000000 0.000000
## AAACGAACAGCTGGTC-1 AAACGCTAGTCATACC-1
## Xkr4 0.0000000 0.0000000
## Mrpl15 0.0000000 0.7161525
## Lypla1 0.5281736 0.7161525
## Tcea1 2.1126943 2.8646100
## Rgs20 0.0000000 0.0000000
df3 <- data.frame(total_counts = total_counts,
sample = sce_macs$sample,
sorting = sce_macs$sorting_day,
lane = sce_macs$lane,
condition = sce$condition)
df4 <- data.frame(
log2_total_counts = log2(total_counts),
#log2_total_features = log2(total_features),
sample = sce_macs$sample)
make_barplot(df3, df3$sample, df3$total_counts, ylab = "Library size") +ylim(0, 50000)
make_barplot(df4, df4$sample, df4$log2_total_counts, ylab = "Library size (log2)") +ylim(11.5,15.5)
normVarMatrix <- getVarianceExplained(assays(sce_macs)$logcounts, variables = df3[,c("total_counts", "sample", "sorting", "condition")])
## Warning in beachmat_internal_FUN(X[[3]], ...): ignoring 'condition' with fewer
## than 2 unique levels
plotExplanatoryVariables(
normVarMatrix,
variables = variables) +
ggtitle("Young - Mono/macs") +
theme(text = element_text(size=20)) +
scale_color_manual(values=c("dodgerblue" ,"purple" , "green3","orange", "blue", "red"))
## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.
## Warning: Removed 17011 rows containing non-finite values (stat_density).
assays(sce_macs)$limma <- NULL
saveRDS(sce_macs, "/mnt/nmorais-nfs/marta/pB_joana/pC_data/sce-mono-macs-young-without-residents.rds")
Create new seurat object
norm_mat <- (2**assays(sce_macs)$logcounts)-1
## Warning in asMethod(object): sparse->dense coercion: allocating vector of size
## 1.5 GiB
ln_mat <- log(norm_mat + 1)
srt_subset <- CreateSeuratObject(counts = ln_mat, min.cells = 0, min.features = 0)
## Warning: Non-unique features (rownames) present in the input matrix, making
## unique
## Warning: Non-unique cell names (colnames) present in the input matrix, making
## unique
srt_subset
## An object of class Seurat
## 16864 features across 12313 samples within 1 assay
## Active assay: RNA (16864 features, 0 variable features)
srt_subset <- FindVariableFeatures(srt_subset, selection.method = "vst", nfeatures = 3000)
# Identify the 20 most highly variable genes
top20 <- head(VariableFeatures(srt_subset), 20)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(srt_subset)
plot2 <- LabelPoints(plot = plot1, points = top20, repel = TRUE)
## When using repel, set xnudge and ynudge to 0 for optimal results
plot2
## Warning: Transformation introduced infinite values in continuous x-axis
## Warning: Removed 49 rows containing missing values (geom_point).
all.genes <- rownames(srt_subset)
srt_subset <- ScaleData(srt_subset, features = all.genes)
## Centering and scaling data matrix
srt_subset <- RunPCA(srt_subset, features = VariableFeatures(object = srt_subset), npcs = 100)
## PC_ 1
## Positive: Itm2b, Apoe, Selenop, Pltp, C1qc, C1qb, C1qa, Timp2, Ms4a7, Hexa
## Rgs10, Hexb, Ctsh, Trem2, Arhgap22, Tmcc3, Tmem86a, Tnfaip8l2, Frmd4b, Otulinl
## Itga9, Ypel3, Cxcl16, Gm10076, Fcrls, Cox7a2l, Mertk, Serpinb6a, Abcg1, Ptms
## Negative: Clec4e, Clec4d, Pim1, Fn1, Ehd1, Thbs1, Ccl6, Vcan, Ccl9, Ahnak
## S100a6, Rab11fip1, Ppp4r2, Cd14, Sod2, F10, S100a4, Adora2b, Pkm, Emilin2
## S100a10, Lilrb4a, Cxcl2, Plcb1, Ptgs2, Gda, Por, Chil3, Mdm2, Ccr1
## PC_ 2
## Positive: Plbd1, Ly6e, Cd74, Plac8, Arhgap15, Samhd1, Dleu2, Ifitm6, Gpr141, Etv6
## Ifi209, Btg2, Sik3, Arhgap26, Napsa, Ccr2, Cybb, H2-Ab1, Txnip, H2-Eb1
## Ccnd3, Ms4a4c, Ifi213, H2-Aa, Ptprj, Cd52, A330040F15Rik, Sell, Mndal, Sp140
## Negative: Ctsd, Ctsb, Fabp5, Lgals1, Ctsl, Cd63, Cd68, Pf4, Spp1, Ftl1
## Gpnmb, Aprt, Trem2, Syngr1, Grn, Vat1, Cstb, Lhfpl2, Slc6a8, Arhgap10
## Emp1, Cd93, Lgals3, Pgam1, Cd36, Fth1, Gde1, Capg, Ms4a7, Timp2
## PC_ 3
## Positive: Tmsb10, S100a6, S100a4, Rnh1, Ifitm3, Ly6c2, Lgals3, Ms4a4c, Plac8, Lsp1
## Msrb1, Crip1, Capg, Zbp1, S100a11, Lgals1, Ifi204, Sell, Bst2, Phf11b
## Isg15, Ifi27l2a, Ifitm6, Txn1, Anxa2, Samhd1, Ifi209, Cd68, Oas3, Hp
## Negative: Tnfaip3, Nfkbiz, Tnf, Nfkb1, Cxcl2, Gm15726, Nfkbia, Mthfs, Cd83, Nlrp3
## Sdc4, Abca1, Bcl2a1b, Maff, Rapgef2, Kansl1l, Hivep2, Kctd12, Tank, Sash1
## Junb, Gadd45b, Ccrl2, Kdm6b, Pde4b, Phlda1, Il10, Nrp2, Mir155hg, Ptprj
## PC_ 4
## Positive: Cytip, Xylt1, Fyn, Runx3, Gpr132, Antxr2, Nfe2l2, Pde3b, Nuak2, Arl4c
## Gsr, Itgal, Csf2rb, Sorl1, Trps1, Gngt2, Havcr2, Ace, Itgax, Crem
## Mxi1, Nfil3, Plxnc1, Mrtfa, Lmnb1, Gcnt2, Stap1, Gfod1, Tspan13, Runx2
## Negative: Isg15, Fcgr1, Ccl2, Rsad2, Ifi211, Ccl7, Ifit3, Slfn5, Ccl12, Oasl2
## Usp18, Oasl1, Irf7, Zbp1, Ifit2, Ifit1, Ifi204, Slfn4, Ifi203, Ifi205
## Mndal, Phf11d, Phf11b, Rnf213, Slfn1, Ifi209, Bst2, Trim30a, Ifi47, Rtp4
## PC_ 5
## Positive: Pid1, Ccnb1ip1, Zswim6, Gm26917, Zfp608, Txnip, F13a1, Anxa2, Gm26887, Fn1
## Grk5, Camk1d, Peak1, Thbs3, Cmss1, Dand5, Baiap2, Tmem164, Dlg4, Fbxl18
## Dleu2, Pecam1, Slc7a5, Sgms1, Zfp710, Ints6, S100a4, Ahnak, Pde4c, S100a10
## Negative: Irf7, Bst2, Ifit3, Rsad2, Lgals3bp, Axl, Cd52, Ifit2, Phf11b, Oasl2
## Isg15, H2-Aa, Ms4a4c, AW112010, Aif1, Slamf7, H2-Ab1, Ifi47, Fth1, Oas3
## H2-Eb1, Mndal, Cd74, Ly6e, Fcgr4, Slfn5, Ifi211, Znfx1, Trim30a, Ly6a
ElbowPlot(srt_subset, ndims = 100) +
geom_vline(xintercept = 25, color = "red", alpha = 0.5) +
theme(text = element_text(size = 10),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10))
srt_subset$sample <- sce_macs$sample
srt_subset$lane <- sce_macs$lane
srt_subset$sorting_day <- sce_macs$sorting_day
srt_subset$cell_type <- sce_macs$cell_type
DimPlot(srt_subset, reduction = "pca", group.by = "cell_type", cols = mac_colors)
srt_subset <- RunUMAP(srt_subset, n.components = 10, features = VariableFeatures(srt_subset))
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 17:43:52 UMAP embedding parameters a = 0.9922 b = 1.112
## 17:43:52 Read 12313 rows and found 3000 numeric columns
## 17:43:52 Using Annoy for neighbor search, n_neighbors = 30
## 17:43:52 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 17:44:03 Writing NN index file to temp file /tmp/Rtmpz4hIrB/file66be2a68b65a
## 17:44:03 Searching Annoy index using 1 thread, search_k = 3000
## 17:46:39 Annoy recall = 100%
## 17:46:40 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 17:46:42 Initializing from normalized Laplacian + noise (using irlba)
## 17:46:42 Commencing optimization for 200 epochs, with 663648 positive edges
## 17:46:53 Optimization finished
DimPlot(srt_subset, reduction = "umap", group.by = "cell_type", cols = mac_colors)
srt_subset <- RunTSNE(srt_subset,
dim.embed = 3,
dims = 1:25)
DimPlot(srt_subset, reduction = "tsne", group.by = "cell_type", cols = mac_colors)
DimPlot(srt_subset, reduction = "tsne", group.by = "sample", cols = mac_colors)
DimPlot(srt_subset, reduction = "tsne", group.by = "sorting_day", cols = mac_colors)
DimPlot(srt_subset, reduction = "tsne", group.by = "lane", cols = mac_colors)
saveRDS(srt_subset, "/mnt/nmorais-nfs/marta/pB_joana/pC_data/srt-mono-macs-young-without-residents.rds")
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.6 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] scran_1.22.1 scater_1.22.0
## [3] scuttle_1.4.0 SingleCellExperiment_1.16.0
## [5] SummarizedExperiment_1.24.0 Biobase_2.54.0
## [7] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
## [9] IRanges_2.28.0 S4Vectors_0.32.4
## [11] BiocGenerics_0.40.0 MatrixGenerics_1.6.0
## [13] matrixStats_0.62.0 ggplot2_3.3.6
## [15] SeuratObject_4.1.3 Seurat_4.1.0
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.7 igraph_1.3.5
## [3] lazyeval_0.2.2 sp_1.5-0
## [5] splines_4.1.2 BiocParallel_1.28.3
## [7] listenv_0.8.0 scattermore_0.8
## [9] digest_0.6.30 htmltools_0.5.3
## [11] viridis_0.6.2 fansi_1.0.3
## [13] magrittr_2.0.3 ScaledMatrix_1.2.0
## [15] tensor_1.5 cluster_2.1.4
## [17] ROCR_1.0-11 limma_3.50.3
## [19] globals_0.16.1 spatstat.sparse_3.0-0
## [21] colorspace_2.0-3 ggrepel_0.9.1
## [23] xfun_0.34 dplyr_1.0.10
## [25] RCurl_1.98-1.9 jsonlite_1.8.3
## [27] progressr_0.11.0 spatstat.data_3.0-0
## [29] survival_3.4-0 zoo_1.8-11
## [31] glue_1.6.2 polyclip_1.10-4
## [33] gtable_0.3.1 zlibbioc_1.40.0
## [35] XVector_0.34.0 leiden_0.4.3
## [37] DelayedArray_0.20.0 BiocSingular_1.10.0
## [39] future.apply_1.9.1 abind_1.4-5
## [41] scales_1.2.1 edgeR_3.36.0
## [43] DBI_1.1.3 spatstat.random_3.0-1
## [45] miniUI_0.1.1.1 Rcpp_1.0.9
## [47] viridisLite_0.4.1 xtable_1.8-4
## [49] dqrng_0.3.0 reticulate_1.26
## [51] spatstat.core_2.4-4 rsvd_1.0.5
## [53] metapod_1.2.0 htmlwidgets_1.5.4
## [55] httr_1.4.4 RColorBrewer_1.1-3
## [57] ellipsis_0.3.2 ica_1.0-3
## [59] farver_2.1.1 pkgconfig_2.0.3
## [61] sass_0.4.2 uwot_0.1.14
## [63] deldir_1.0-6 locfit_1.5-9.6
## [65] utf8_1.2.2 labeling_0.4.2
## [67] tidyselect_1.2.0 rlang_1.0.6
## [69] reshape2_1.4.4 later_1.3.0
## [71] munsell_0.5.0 tools_4.1.2
## [73] cachem_1.0.6 cli_3.4.1
## [75] generics_0.1.3 ggridges_0.5.4
## [77] evaluate_0.17 stringr_1.4.1
## [79] fastmap_1.1.0 yaml_2.3.6
## [81] goftest_1.2-3 knitr_1.40
## [83] fitdistrplus_1.1-8 purrr_0.3.5
## [85] RANN_2.6.1 sparseMatrixStats_1.6.0
## [87] pbapply_1.5-0 future_1.28.0
## [89] nlme_3.1-160 mime_0.12
## [91] compiler_4.1.2 rstudioapi_0.14
## [93] beeswarm_0.4.0 plotly_4.10.0
## [95] png_0.1-7 spatstat.utils_3.0-1
## [97] statmod_1.4.37 tibble_3.1.8
## [99] bslib_0.4.0 stringi_1.7.8
## [101] highr_0.9 bluster_1.4.0
## [103] lattice_0.20-45 Matrix_1.5-1
## [105] vctrs_0.5.0 pillar_1.8.1
## [107] lifecycle_1.0.3 spatstat.geom_3.0-3
## [109] lmtest_0.9-40 jquerylib_0.1.4
## [111] BiocNeighbors_1.12.0 RcppAnnoy_0.0.19
## [113] data.table_1.14.4 cowplot_1.1.1
## [115] bitops_1.0-7 irlba_2.3.5.1
## [117] httpuv_1.6.6 patchwork_1.1.2
## [119] R6_2.5.1 promises_1.2.0.1
## [121] KernSmooth_2.23-20 gridExtra_2.3
## [123] vipor_0.4.5 parallelly_1.32.1
## [125] codetools_0.2-18 MASS_7.3-58.1
## [127] assertthat_0.2.1 withr_2.5.0
## [129] sctransform_0.3.5 GenomeInfoDbData_1.2.7
## [131] mgcv_1.8-41 parallel_4.1.2
## [133] beachmat_2.10.0 grid_4.1.2
## [135] rpart_4.1.19 tidyr_1.2.1
## [137] DelayedMatrixStats_1.16.0 rmarkdown_2.17
## [139] Rtsne_0.16 shiny_1.7.2
## [141] ggbeeswarm_0.6.0